Abstract
Currently, soil-moisture data extracted from microwave data suffer from poor spatial resolution. To overcome this problem, this study proposes a method to downscale the soil moisture spatial resolution. The proposed method establishes a statistical relationship between low-spatial-resolution input data and soil-moisture data from a land-surface model based on a neural network (NN). This statistical relationship is then applied to high-spatial-resolution input data to obtain high-spatial-resolution soil-moisture data. The input data include passive microwave data (SMAP, AMSR2), active microwave data (ASCAT), MODIS data, and terrain data. The target soil moisture data were collected from CLDAS dataset. The results show that the addition of data such as the land-surface temperature (LST), the normalized difference vegetation index (NDVI), the normalized shortwave-infrared difference bare soil moisture indices (NSDSI), the digital elevation model (DEM), and calculated slope data (SLOPE) to active and passive microwave data improves the retrieval accuracy of the model. Taking the CLDAS soil moisture data as a benchmark, the spatial correlation increases from 0.597 to 0.669, the temporal correlation increases from 0.401 to 0.475, the root mean square error decreases from 0.051 to 0.046, and the mean absolute error decreases from 0.041 to 0.036. Triple collocation was applied in the form of [NN, FY3C, GEOS-5] based on the extracted retrieved soil-moisture data to obtain the error variance and correlation coefficient between each product and the actual soil-moisture data. Therefore, we conclude that NN data, which have the lowest error variance (0.00003) and the highest correlation coefficient (0.811), are the most applicable to Qinghai Province. The high-spatial-resolution data obtained from the NN, CLDAS data, SMAP data, and AMSR2 data were correlated with the ground-station data respectively, and the result of better NN data quality was obtained. This analysis demonstrates that the NN-based method is a promising approach for obtaining high-spatial-resolution soil-moisture data.
Highlights
Moisture stored in surface soil accounts for less than 0.001% of total global freshwater by volume but plays an important role in connecting global terrestrial water, energy, and carbon cycling processes [1]
The neural network (NN) model was trained on a small subset of the available dataset, the entire dataset was used for retrieval and evaluation
The results demonstrate that downscaling the soil moisture (SM) captures better the variations in precipitation over time, which indicates that the downscaled SM
Summary
Moisture stored in surface soil accounts for less than 0.001% of total global freshwater by volume but plays an important role in connecting global terrestrial water, energy, and carbon cycling processes [1]. By influencing soil evaporation and transpiration, soil moisture (SM) strongly affects the interaction between the land surface and the atmosphere [2]. A thorough understanding of SM can contribute to efficient monitoring of the climate and environmental changes and provide valuable guidance for drought monitoring and flood forecasting in agriculture and forestry [3]. SM determines the distribution of precipitation infiltration and surface runoff, which controls plant growth [4]. High-quality SM data is crucial in multiple technological fields, such as hydrology, meteorology, climatology, and water-resources management. High-quality SM data is crucial in multiple technological fields, such as hydrology, meteorology, climatology, and water-resources management. 4.0/).
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